Global Stock Market Integration and the Determinants of Co-movements: Evidence from developed and emerging countries

Author(s): Asma Mobarek1

Abstract

This study examines the extent of cross-country returns co-movement between the stock

markets of five developed benchmark countries [US, UK, Japan, Germany and France] and five

emerging benchmark countries [Brazil, Russia, India, China and South Africa] countries, vis-à-vis

a total country sample composed by 20 countries. The Geweke (1982) Measure of Feedback

methodology along with a set of pooled cross-country time-series regressions is used to identify

and explain the changes in stock market integration. The general findings for the Geweke

contemporaneous feedback measures provide supportive evidence of increased stock market

integration. Our pooled cross-country time-series regression analysis has shown that countries’

economic integration, as measured by the explanatory variables explain almost 32 percent of

the variation in the contemporaneous Geweke feedback measure on a global scale. This

explanatory power becomes stronger for the group of developed markets (49.74 percent) and

for countries that are part of the European economical and political union (69.82 percent). The

results also reported that several variables as significantly associated with the evolution of stock

markets integration over time. These statistically significant variables include, on a global level,

import dependence, stock markets’ size differential and their relative size, difference in annual

GDP growth rate as well as the time trend.

1 Associate Professor, Stockholm University Business School. The author acknowledges NASDAQ OMX Foundation for financially supporting the project. The author also thankful to Angelo Fiorante and also Federica Vitali, for their efforts as a research assistant in the project.

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1. Introduction

The movement towards a synchronized stock market landscape has gained momentum,

especially during the past two decades, where tighter economical and financial linkages among

developed economies have grown stronger. However, the rises of many important emerging

markets, which have been a major driver of global growth the past decades, have opened up

additional channels for cross-border relations. Other causes behind the rapid increase in world

trade, capital movements, and foreign investments between world economies are due to

market liberalization/deregulation, technological advances and removals of statutory controls.

Many of these factors have contributed to more interlinked economies, which in turn, are said

to have given rise to a higher degree of stock market synchronization, especially in volatile time

periods, e.g. eruption of a financial crisis, war, or political instability. The aftermath of historical

financial crises, including the latest one in 2007, have opened up a tremendous interest for

determining the underlying factors that might explain how stock markets are correlated with

one and other for better understanding the causes of the sudden and simultaneous

deterioration of wealth that occurs during crises periods. To investigate the propensity of one

country to be affected by global shocks have enormous value for preventing future crises. The

extent of financial and economical integration between a country-pair may indeed be reflected

by the degree of stock markets co-movement that they exhibit. In fact, the dynamic structures

of international economies have clearly intensified the complexity behind stock market

performances. As our countries become more economically interlinked, explaining the

formation of price co-movement between stock markets on an international level is significant

for better understanding this higher interdependency and integration. However, the

contemporary research in stock market integration has not sufficiently focused on determining

the driving forces behind co-movement although this information would be most effective for

policy-makers and investors that are keen to know how economic linkages may influence the

countries financial stability, diversification possibilities and what types of common and specific

shocks stock markets are most vulnerable against. This study assesses how stock market

integration and the co-movement between country-pairs, distinguishing between developed

and emerging markets, has been affected in terms of timing and intensity during 1995 – 2010. A

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greater degree of co-movements in stock prices is seen as a reflection of greater stock market

integration. It also takes a closer look at the outcome of significant financial meltdowns

occurring within this period, e.g., the Asian crises, the dotcom-bubble, the financial crisis of

2007 and other country-specific crises. It investigates plausible economical and financial

underlying factors that are said to characterize and influences the extent of integration

between pairs of countries. A two-step analysis is employed to assess which underlying factors

can explain stock market integration and the degree of co-movement. First, the Geweke (1982)

feedback measure, outlined in Section 3.1, is estimated between country pairs. By considering a

dynamic interrelationship between two countries’ daily stock market returns, the year-on-year

feedback measures demonstrate how co-movement evolves over time, reflecting changes in

stock market integration by increases or decreases in the measures (see Bracker et al., 1999;

Johnson & Soenen, 2002, 2003). Second, the estimated feedback measures are employed in a

pooled cross-country time-series regression, outlined in section 3.2, including significant

economical, financial and country-specific factors hypothesized to influence the degree of stock

market integration. The data sample covers 20 countries - ten developed and ten emerging –

from 1995 to 2010.

The major findings of the paper are as follows: The general findings for the Geweke

contemporaneous feedback measures provide supportive evidence of increased stock market

integration. A reasonably clear time trend is identified, where the extent of contemporaneous

co-movement across markets has intensified over time, especially for emerging countries,

which consequently suggests that greater market efficiency is being fostered at the

international level. On the other hand, the results of the Geweke unidirectional feedback

measures indicate a tendency that some markets are more likely to lead other markets than

vice versa. However, there is a less distinctive time trend in the movements of the annual two-

ways unidirectional feedback measures, suggesting that leader-follower relationships are likely

to change over time periods. These alterations might be due to possible changes in a country’s

economy and market conditions, but also the stability of global markets. Nevertheless, the

highly sophisticated market of the US and the emerging markets of Brazil and Russia appear to

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affect other rather than be affected. However, the distinction with previous studies is that not

only is the contemporaneous measures larger on average, but higher significance levels are

reported for the unidirectional measures of feedback, suggesting that cross-market adjustment

persist over time more often than occasionally for this study’s time period 1995-2010. The

study also reports that countries’ economic integration, can explain almost 32 percent of the

variation in the contemporaneous Geweke feedback measure on a global scale over the 15-year

period, 1995-2009. This explanatory power becomes stronger for the group of developed

markets (49.74 percent) and for countries that are part of the European economic and political

union (69.82 percent). The results also point out several variables as significantly associated

with the evolution of stock markets integration over time. These statistically significant

variables include, on a global level, import dependence, stock markets’ size differential and

their relative size, difference in annual GDP growth rate as well as the time trend.

The rest of the paper is organized as follows: section 2 reviews the literature on stock market

integration, section 3 outlines the already mentioned research method more in detail, section 4

presents the empirical results, and section 5 provides the conclusions.

2. Literature review:

The current state of literature offers numerous studies that examine the presence of stock

market integration, with the notion that markets have been exhibiting tighter co-movements

with one and other, and that they are more integrated than never due to closer financial and

economical linkages. However, it is clear that less has been said concerning the determinants of

stock market co-movement and economic integration, which makes it still an intriguing

research topic where there seems to be many missing pieces of the puzzle.

A good number of studies on the correlation between stock markets at an international level

have been concerned with measuring the extent and direction of the co-movement by using

multivariate GARCH, vector auto-regression (VAR), Unit root test, and various co-integration

tests. Several of these studies report that during periods of financial crisis the stock market co-

movement is greater than before the crisis occurred. Liu et al. (1998) employs a vector

autoregressive analysis to examine the dynamic structure of international transmission in stock

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returns for six countries – the U.S, Japan, Hong Kong, Singapore, Taiwan and Thailand – for the

period 1985-1990 capturing the October 1987 stock market crash. They conclude that the

degree of interdependence among the Asian-Pacific markets increased substantially after the

1987 stock market crash and where the U.S market possesses an influential role affecting these

markets. In addition, the risk reduction benefits of international portfolio diversification have

been reduced due to the higher interdependence that has been observed in these markets.

Similarly, Arshanapalli et al. (1995), conclude that the co-integration structure that links these

markets increased substantially after the 1987 collapse. However, Longin & Solnik (1995)

examines the correlation for seven major European countries over the period 1960-90

indicating that not only is the international covariance and correlation matrices unstable over

time, but that correlation rises in periods when the conditional volatility of markets is large.

Karolyi & Stulz (1996) explores the co-movements between the Japanese and U.S stock markets

from 1988-1992, showing that correlation and covariance are high when markets move a lot,

hence demonstrating the shortcomings of international diversification in times of high volatility

which is when it is most needed. In light of the benefits of international portfolio diversification

(see e.g. Solnik, 1995), there is a range of studies that deals with emerging stock markets, which

are said to have lower exposure to world factors, thus having lower levels of integration and

therefore may offer greater opportunities for risk diversification across countries. Moreover,

Ampomah (2008) presents evidence that African stock markets are still segmented from global

markets offering strong diversification benefits.

Another type of studies has provided evidence on which markets dictates over other markets.

An early study by Eun & Shim (1989) highlights the influence and power that the U.S stock

market has on the stock markets of eight other developed countries. Findings indicate that a

substantial amount of interdependence exists, where the U.S stock market represents the most

influential world economy having by far a dominant position when it comes to producing

valuable information that affects world stock markets. Empirically they found that innovations

in the US stock market were rapidly transmitted to the rest of the world, whereas innovations

in other markets did not have much effect on the US market.

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A very few studies evidence on the determinants of stock market co-movement has been

presented by Pretorius (2002), which examined ten emerging stock markets for the period

1995–2000 by employing a cross-section and a time-series model. The major findings showed

that only bilateral trade and the industrial production growth differential were significant for

explaining the correlation between two countries on a cross-sectional basis. Similar results

were achieved by the time-series regression. The model explained 40% of the variation in the

correlation coefficient, thus 60% could be due to contagion or other explanatory variable that

was not included in the analysis. Forbes & Rigobon (2002) pointed out that traditional tests for

contagion based on cross-market correlation coefficient are problematic due to the bias

introduced by changing volatility in market returns, i.e. heteroskedasticity. During a crisis

period when stock market volatility increases, the estimates of cross-market correlation will be

biased upward. The paper reevaluates several crisis periods with a method that corrects for this

heteroskedasticity, finding that there was no contagion during these periods of turmoil. They

conclude that the higher levels of market co-movement during the observed periods are mostly

due to interdependence, which depend on the linkages that economies have with each other.

Wälti (2005) follows Forbes and Rigabon (2002) correction model for determining the

macroeconomic variables underlying co-movements between stock market return for fifteen

industrialized countries for the period 1973–1997. Results show that trade and financial

integration contributes positively to stock market synchronization, while a fixed exchange rate

regime increases co-movements. Other factors such as the similarity of economic structure

across countries, informational asymmetries and a common language also contribute to stock

market synchronization. Serra (2000) found that emerging markets’ returns are mainly driven

by country specific factors and less by industry specific factors. Cross-market correlation is not

affected by the industrial composition of the indices, making cross-market diversification a

better option than cross-industry diversification. However, significant loss of diversification

benefits may occur if the industrial mix is totally ignored. Morgado & Tavares (2007) examines

the impact of bilateral indicators of economic integration on the correlation of stock return of

40 developed and emerging markets for the period 1970–1990. Results showed that bilateral

trade intensity affects the correlation positively, whereas the asymmetry of output growth, the

dissimilarity of export structure and the real exchange rate volatility have negative effects on

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stock return correlation. Lin & Cheng (2008) apply a non-linear Multinominal Logit Model

(MNLM) in which co-movement is categorized in three outcomes: (i) negative co-movements,

(ii) positive co-movements and (iii) no co-movements. The empirical analyze of the economic

determinants that affect the stock market co-movement relationship between Taiwan and its

four major trading partners (Mainland China, US, Japan and Hong Kong) are stock market return

volatility, the rate of change in exchange rate and interest rate differentials.

Other types of studies, such as Bracker & Koch (1999) suggest that countries that experience

greater economic integration should also experience greater co-movement in their respective

capital markets. Their study addresses questions whether, how and why, the correlation

structure changes over time. By testing the stability of the correlation matrix over different

periods and modeling potential economic determinants of the correlation structure for ten

national stock indices during 1972–1993, they provide evidence of the dramatic evolution in the

correlation matrix over both short- and long-time horizon. Results indicate that the degree of

international integration (measured as the magnitude of the correlation structure) is positively

associated with (1) world market volatility and (2) trend; while it is negatively related to (3)

exchange rate volatility, (4) term structure differential across markets, (5) real interest rate

differentials, and (6) the return on a world market index. However, it is concluded that further

analyzes on potential economic determinants of the correlation structure is needed to fully

understand what makes market move in tandem. Other similar studies like, Bracker et al.,

(1999); Johnson & Soenen, (2002, 2003) investigate how and why different pairs of

international stock markets display differing degree of co-movement over time. The main

empirical results from these studies show that; (Bracker et al., 1999) Several macroeconomic

factors are significantly associated with the extent of stock market integration over time, e.g.

trade, geographic distance, stock market size differential, time trend, and real interest

differential; (Johnson & Soenen, 2002) Asian stock markets become more integrated with the

Japanese stock market over time, especially since 1994, where increased export share from

Asian economies to Japan and greater foreign direct investment from Japan to other Asian

economies contributes to greater co-movement; (Johnson & Soenen, 2003) Indicating that a

high share of trade with the US has a strong positive effect on stock market co-movements for

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equity markets of the Americas, whereas increased bilateral exchange rate volatility and a

higher ratio of stock market capitalization relative to the US contribute to lower co-movement.

In short, the main emphasis of previous mentioned studies has been determining how

integrated markets are by examining the extent of the co-movements that stock markets

exhibit. By flipping the coin, we find a smaller amount of studies that attempt to determine why

stock markets are integrated. The main objective of this research paper is to fill the gap is to

unfold the determinants and the driving forces behind stock market relationships including

both developed and emerging markets, which may indeed be of greater value for investors that

struggles with portfolio-diversification choices and for policy-makers and regulatory bodies that

are keen to know what types of determinants and treaties with other countries that might

affect the national stock market, especially during turmoil periods.

3. Methodology and Data Sample

3.1 Measuring Stock Market Integration: Geweke (1982) Measures of Feedback

Geweke (1982) provides a cardinal methodology for measuring the degree of co-movement (or

interrelationship) between pairs of stock markets, which indicates how integration between

country pairs evolves over time (Bracker et al., 1999; Geweke, 1982, 1984). An increase (or

decrease) in the year-to-year feedback measure reflects an increase (or decrease) in the extent

of stock market integration. The measure of feedback technique has been chosen since it has

certain advantages over other means (e.g. VAR2 or Granger Causality3) that might be used for

testing the relationship between two stock markets. It identifies not only the presence of

significant information flows between two markets, but also the extent of this feedback.

Moreover, it reveals how integration, as well as how the leader/follower relationship changes

over time.

2 “The VAR approach is deficient in its failure to incorporate potential long-term relations and, therefore, may

suffer from specification bias”. (Mukherjee & Naka, 1995) 3 The Granger Causality (1969) test for casual relation can only reveal if the hypothesis under consideration holds

or not.

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The objective of implementing Geweke’s feedback measures on stock market co-movement is

to capture the degree to which daily stock returns (i) move together in the two countries on the

same day and (ii) the degree to which daily stock returns in the two countries lead and lag each

other. The Geweke contemporaneous and unidirectional feedback measures are calculated

annually between pairs of countries using daily stock market returns. In the first stage of the

analysis, the model specification considers a dynamic interrelationship between the daily stock

market return of country i and j, (rit and rjt), to hypothetically depend upon: (i) past returns in

the other market, (ii) its own past returns, and (iii) the idiosyncratic noise. The restricted

regression equations (1) and (2) are specified as follows:

rit 0 akrjtk k1

M 2

bkritkk1

M 1

it, var( it) i2

[1]

rj t 0 ckri tk k1

M 2

dkrj tkk1

M 1

j t, v ar( j t) j

2

[2]

wi t h t he var i ance- covar i ance m at r i x of r es i dual s (i t and j t) :

C o v i t

j t

i2 i j

i jj2

Y

Y D e t e r m i n a n t o f c o v a r i a n c e m a t r i x Y c o v (i t, j t)

The residuals εit and εjt are assumed to be white noise, i.e., normally distributed N(0,σεz2),

where z = i or j and Cov(εzt, εz,t-1)=0. Despite the fact that the residuals εit and εjt are

assumed to be serially uncorrelated, they may exhibit contemporaneous correlation4 with each

other. The regression equations [1] and [2] can be solved by applying the Seemingly Unrelated

Regression (SUR), a technique that account for the contemporaneous correlation among the

residuals (Judge et al., 1988). The initiative behind Eq. [1] and [2] measures the nature and

extent of the interrelationship between daily stock returns in the two countries, e.g. coefficient

4 Estimates derived with OLS techniques may be inefficient when error terms may exhibit contemporaneous correlation. See Zellner (1962).

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ak display how the second market (j) leads the first market (i) across days, while coefficient ck

display how the first market (i) leads the second market (j) across days (Bracket et al., 1999).

Following Bracket et al. (1999) the lag length of M1 and M2 are chosen to be 10 and 5 business

days, respectively. In the second stage of the analysis, it is assumed that there is no

interrelationship among the price series of the two different stock markets, (i.e. coefficients ak

and ck will be equal to zero for k = 1,2,…,M2) hence, the unrestricted regression equations [3]

and *4+ incorporates only the country’s own lagged returns to explain its current daily return,

and they can be estimated with ordinary least squares (OLS).

rit =a0

´ + bk

´rit-k

k=1

M1

å +mit, var(mit ) =s mi

2

[3]

rjt = b0

´ + dk

´rjt-k

k=1

M1

å +m jt, var(m jt ) =s m j

2 [4]

The residuals µit and µjt are independently and identically distributed with zero means and

variance, where z = i or j, and

Cov(i,t , j,t ) 0 i.e., the residuals does not exhibit

contemporaneous correlation, thus Ordinary Least Squares (OLS) technique is appropriate for

solving Eq. [3] and [4]. At this stage, three null hypotheses may be identified from the

considerations related with the above analysis. They are formulated as follows:

H1: There is no contemporaneous relation between rit and rjt on the same day.

H2: There is no unidirectional relationship from rjt to rit across days (i.e. ak = 0, for any k)

H3: There is no unidirectional relationship from rit to rjt across days (i.e. ck = 0, for any k)

According to Geweke (1982) Measure of Feedback, the interrelationship among the stock

markets of two different countries can be measured by the following Log-likelihood Ratio

statistics:

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GMFi* j = (n)ln s mi

2 ´s m j

2( ) / Yéë

ùû ~

a

c1

2 under H1;

GMFj®i = (n)ln s mi

2 /s ei

2( ) ~a

cM 2

2 under H2;

GMFi® j = (n)ln s m j

2 /s e j

2( ) ~a

cM 2

2 under H3;

The yearly Geweke measures demonstrate how the co-movement of daily returns between a

pair of countries evolves over time, e.g. where an increase (or decrease) in GMF from year t1 to

t1+n (n=1,2…T) reflects an increase (or decrease) in the extent of stock market integration for

that pair of countries. The likelihood-ratio test statistics forms the Geweke feedback measure,

and it is calculated for each country pair and for each year from the residual variances and co-

variances from the restricted [Eq. 1 and 2] and unrestricted [Eq. 3 and 4] country pair

regressions.

3.2 Modeling for Determinants: Pooled Cross-Country Time-series Regression

The second step of this analysis is specifically aimed at investigating the statistical significance

of various macroeconomic and financial factors, indicators of economic integration between

two countries, in explaining the evolution of the degree of co-movement between their stock

markets over time. At this purpose, a pooled cross-country (more specifically, cross-country

pair) time-series regression has been estimated with the contemporaneous Geweke measure of

feedback CGMFij,t for countries i and j at time t acting as dependent variable, across pairs of the

20 countries included in the study. The pooled regression model representing the potential

determinants of equity markets interdependence takes the following form:

CGMFij,t 0 aTradeta

a1

A

bMacrotb

b1

B

cDevelopmenttc Trendt t

c1

C

[5]

The explanatory variables included in the regression model [5] outlined above are described in

Table 1.

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Table 1. Potential determinants of stock market integration

Xi = (Xij /Xi )t

Xj = (Xji /Xj )t

Mi = (Mij /Mi )t

Mj = (Mji /Mj )t

I = (πi -πj )t

RI = (ri -rj )t

Gr = (gi -gj)t

S = (sizei -sizej )t

MV = (MVj /MVi )t

Percent of world equity market share of country i minus that in j

Ratio of stock market capitalization of country j to that of country i , expressed in US dollars

T Variable for the time trend t (i.e. t = 1,2,…,T years)

(C.) Measure of financial development

Inflation differantial between markets i and j

Real interest rate differential between markets i and j

Imports of country i from country j , relative to i 's total import

(A.) Measures of the nature and extent of bilateral trade relationships

Exports from country i to country j , relative to i 's total export

Exports from country j to country i , relative to j 's total export

GDP annual growth rate differential between country i and j

Imports of country j from country i , relative to j 's total import

(B.) Macroeconomic factors

In terms of bilateral trade relationships, four different variables have been considered in order

to encompass the point of view of both countries in each pair, hence revealing the two

different sides of the same coin. Indeed, although theoretically total exports from country i to j

should equal total import of country j from i, Xij=Mji, the measures used in this study are relative

measures, as also specified by Bracker et al. (1999). Export from country i to j is compared to

country i’s total export and, vice versa, import of country j from i is compared to j’s total

import, so that the theoretically same amount of bilateral trade Xij=Mji becomes relatively more

important for one of the two trading parties. The importance of including four measures of

bilateral trade relationships relies in the fact that each of the four could have a different impact

on the co-movement of two given stock markets. While export from the point of view of both

countries seems to be always positively related to the sensitiveness of one country’s stock

market to its partner’s stock market activities, the same is not valid for import. As fully

explained by Bracker et al. (1999), stock market performance is considered an indicator of the

future economic outline of a country, so that the possibilities of increasing export to that

country should always be positively linked to its stock market movements. On the other hand,

an increasing import dependence of country i on j (and vice versa) may entail positive stock

market co-movements whereas a decreasing dependence may generate a negative effect.

Indeed, when the economy of the importing country performs well, this country is likely to

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import more from its partner thus boosting the latter’s economic performance as well. Hence,

larger import dependence between two countries should be positively associated with greater

co-movements between their stock markets. A reduction in the import dependence may boost

the ability of exporting firms in the less dependent importing country to compete on the global

market with the exporting firms of its partner country, thus driving their stock markets apart.

Hence, the degree of relative import dependence may have either a positive or a negative

effect on stock markets integration. The macroeconomic factors included, inflation rate

differential, real interest rate differential and GDP annual growth rate differential, are expected

to be negatively related to the co-movements in a stock markets’ pair. Indeed, the larger these

differences become, the larger the divergence between the economies of the two countries

and hence the less their stock markets will be influenced by each other. The third group of

variables includes indicators of the stage of stock markets’ development, such as stock markets

size differential and relative size. More specifically, the stock market capitalization of a country

may be a measure of the ease or difficulty, in terms of liquidity and costs, of trading on that

stock market. While a large difference in market size for a pair of countries may determine less

co-movements between their respective stock markets, the relative size of the two markets in

the pair have opposite effects. Last, a time trend is included in the regression to encompass the

possibility that stock market inter-dependence has increased over time, due to the advanced

communications technology, the eased flow of information, trade and capital across borders

and the increasing cross-listing of stocks and mergers between stock markets of different

countries.

As a preliminary test of poolability, we found the applicability of pooled regression model using

the following, (Kunst, 2009):

H01: yit= α + βXit + vit………………………………………………………………………………………………………………….(6)

H11: yit= α + βXit + µi + vit…………………………………………………………………………………………………………(7)

Or H01: µi=0 i=1995, 1996,……….2009

The test Statistic is

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F = ……………………………………………………..(8)

Follows F distribution with (T-1), (N-1)(T-K) df

SSR = Residuals sum squares under the null hypothesis

SSU= Residuals sum squares under the alternative hypothesis

T= Number of years, N= Number of observations, K= Number of parameters=10

F=

Our regression analysis extends the works of Braker et al. (1999) and of Johnson and Soenen

(2002; 2003) in one fundamental way, which is the large span of countries included in our

analysis. Whereas the number of explanatory variables considered is on average the same, no

previous study has incorporated as many as twenty countries. In addition, the regression model

presented above has been estimated for groups of markets in the same geographical area as

well as by differentiating markets according to the most distinctive characteristic, which is their

level of development.

3.3 Data Sample and Summary Statistics

The 20 countries included in the data sample are specified in Table 2. Out of these 20 countries,

10 are used in the analysis of stock market integration. These are referred as the base country

group, which consists of five developed [US, UK, Japan, Germany and France] and five emerging

[Brazil, Russia, India, China and South Africa] countries. The sample covers a 16-year period

from 1995-2010. Daily stock returns are calculated as the log change in the daily index closing

price as follows:

where z = market i or j, and Pz,t represent the closing price of the markets on day t. The daily

MSCI stock index time-series expressed in US dollars have been extracted from the Thomson

Financial DataStream. The pooled cross-country time-series regression uses yearly data, also

rz tln(Pz,t/Pz,t1)100

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extracted from Thomson Financial DataStream, consisting of cross-sections of country-pair

observations covering a 15-year period from 1995-2009 (2010 has been excluded due to non-

availability of dataset).

Table 2. Description of data sample and price indices

Table 3, provides descriptive statistics for the daily stock index returns for the 20 sample

countries, where the test of normality is rejected for all return series. Table 4, presents the

correlation coefficient of returns. The highest correlation coefficients are mostly found

between developed markets, especially between European countries.

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Table 3: Summary statistics on daily index returns (%)

17

Table 4: Correlation coefficient on daily index

returns

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4. Empirical Results

4.1 Stock market integration analysis: Geweke (1982) Measures of Feedback

The result statistics of the annual Geweke measures of feedback (GMF), contemporaneous and

two-way unidirectional feedback, estimated with regression [1] – [4] during the sample period

1995-2010 between the base countries – United States, United Kingdom, Japan, Germany,

France, Brazil, Russia, India, China and South Africa – vis-à-vis the total country sample (see

section 3.3) are reported in Appendix [A] and summarized in Table [5]. The corresponding

results for the base countries are illustrated in Figures [1] – [3], which summarizes the average

annual GMF across time as well as presenting a marketwise differentiation – total country

sample average, developed country average and emerging country average.

The contemporaneous feedback measures results, summarized in Table [5]: Geweke 1, report

high percentages [94% - 99%] of significance present across the country sample. The highest

average country-pairs contemporaneous feedback measures is found in France, Germany and

the United Kingdom, with each exceeding 100, whereas India’s average is the lowest one at just

over 40, and the remaining eight countries fall in the range of 49-82. The year-on-year

contemporaneous measure of feedback [See Appendix A for selected countries] clearly indicate

that stock market integration have intensified, where larger measures denote greater

contemporaneous relationship between stock return patterns from country pairs. The trend

towards a global stock market landscape that takes into account information flows from other

markets has clearly gain momentum the past decade. Figure [1] reflects this evolution of stock

market integration during the 16-year [1995 – 2010] sample period. It illustrates how stock

markets have been witnessing stronger co-movement with time. The estimated measures

indicate significant inter-market relationship across the base country group and the total

country sample. Although the overall results are highly significant across countries, the

developed markets seem to be more extraordinary affected by each other. As seen from Figure

[1], the average contemporaneous feedback measures for developed markets consistently

exceed the average contemporaneous feedback measures for emerging markets, in particular

for UK, Germany and France.

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Table 5. Summary of Geweke Measure of Feedback (GMF)

According to Johnson & Soenen (2009), this greater extent of stock market integration may be

attributed to the presence of more favorable economic and political climate towards business

in developed markets. Nonetheless, from 2005 and onwards, emerging markets, in particular

Brazil, Russia, China and South Africa, have enjoyed a tightening of co-movement. This greater

extent of co-movement appears to be rightful considering the increased importance of these

countries’ economies. One might argue that emerging markets have become more

sophisticated and efficient with time. Furthermore, the notion that financial crises periods

change the co-moving behaviors of stock markets seems to be also present in the results. A

further investigation of today’s global financial crisis (2007-) and previous economic

meltdowns, e.g. Asian crisis (1997-98) and the dot-com bubble (2000-02), are to a certain

degree reflected by the feedback measures, which are apparent as upward peaks during the

climax period of the crisis, followed by a stabilizing or a slight plunge in the measures. However,

crisis periods seem to foster a new and higher equilibrium level of co-movement, evidence that

are in line with previous studies, e.g. Liu et al. (1998); Longin and Solnik (1995); and

Arshanapalli et al. (1995).

20

Figure 1. Geweke 1 – Contemporaneous feedback measures

Note: Figures summarizes the average annual feedback measures across time and presents a marketwise differentiation – total country sample

average, developed country average and emerging country average. The tables in the appendix provide the result statistics for each country

pair.

21

The average unidirectional feedback measures, summarized in Table [5]: Geweke 2, from each

base country to all others are considerably lower than the contemporaneous feedback levels,

with all of them being within the range [5.2–19.7]. However, a fairly high percentage of the

year-by-year unidirectional feedback measures from each country to the others are significant

at the 5% level, with the minimum being 6% (for Japan) and the maximum being 65% (for Brazil)

and the rest are within 15% - 50%. Surprisingly, it appears that there is more often than

occasionally a delay with which these stock markets fully incorporate information from other

markets. The United States, Brazil and Russia appears to have higher influence compared to

other markets, since the 50%, 65% and 40% significance of the annual unidirectional feedback

measures, respectively, is fairly higher. However, the other base markets, besides Japan and

China, show also a relatively high percentage of significance. Figure [2] illustrates the

unidirectional feedback measures from the ten base countries to the total country sample. As

mentioned above, interesting features are particularly present in US, Brazil and Russia.

Information flows from these markets in particular are demonstrated to be significant across

days. Additionally, the unidirectional feedback measures variation across time appears to

increase substantially across periods of financial meltdowns. The uncertainty arising from

crises, shown by an alteration in volatility, is clearly reflected by the unidirectional feedback

measures, which illustrate how markets continue to exhibit co-movement across days. For

Russia, this is clearly illustrated by the peak in the feedback measure, which represents the

“Ruble crisis” that hit the country in 1998, triggered by the Asian crisis that erupted one year

before. Furthermore, the booming economy of Brazil reveals further how unidirectional

feedback has intensified over the sample period, especially during the years prior to the

financial crisis of 2007. The nature of crises and market uncertainty appears to extend periods

of co-movement between country pairs, which are more pronounced during financial crises

and/or booming years. The results of financial crises or booming economies seem to add

complexity in how efficient markets are able to incorporate or transmit information flows.

However, identifying a clear time trend, as for the contemporaneous feedback measures, the

unidirectional appears to fluctuate more around crisis periods, but the increase does not persist

with time.

22

Note: Figures summarizes the average annual feedback measures across time and presents a marketwise differentiation – total country

sample average, developed country average and emerging country average. The tables in the appendix provide the result statistics for each

country pair.

Figure 2. Geweke 2 – Unidirectional Measure of Feedback (Base country Others)

23

The average unidirectional feedback measures in the opposite direction, summarized in Table

[5]: Geweke 3, from all other markets to each base markets, reveals additional country-specific

differences. Besides reporting relatively lower estimates than the contemporaneous feedback

levels, the number of significances is slightly lower than the previous unidirectional feedback

measure. The range for the mean values is between [23.03-3.68], where Japan, in this case,

have the highest percentage of significant estimates, 56%, followed by China [38%]. In contrast,

the estimates for US, Brazil and Russia, only 19%, 4% and 11%, respectively, are significant.

Hence, these patterns imply that some markets may have greater tendency of leading other

markets, whereas the opposite is true for others. This seems to be the cases for the latter

mention countries, whereas for Japan and China there is a higher tendency that they are being

lead by others. The leader-follower relationship is subtler and less pronounced for UK,

Germany, France, India and South Africa. Although a good number of significant estimates are

reported for the two-way unidirectional feedback measures, the differences for these countries

are less striking. In Figure [3], it is clear that Japan, as of 2007, has been affected to a higher

degree by the delayed influence of the stock markets of other countries. Moreover, South

Africa was also affected in 1998 by what seems to have been spillover effect from the Asian

crisis that started in 1997, which is illustrated by the spike in the average unidirectional

feedback measures from emerging countries.

24

Figure 3. Geweke 3 – Unidirectional Measure of Feedback (Others Base Country)

Note: Figures summarizes the average annual feedback measures across time and presents a marketwise differentiation – total country

sample average, developed country average and emerging country average. The tables in the appendix provide the result statistics for each

country pair.

25

Moreover, the preliminary analysis of F test statistic of pool ability is significant at 1% level as

the tabulated F value with (14, 945) df is 2.10. So the null hypothesis is rejected, which supports

the use of pooled regression model with the dataset.

4.2 Pooled Regression Analysis

The empirical results from the pooled regression analysis over the 15-year period 1995-2009

are presented in Table 5.a for all 190-country pairs, for 45 pairs of developed countries and 45

pairs of emerging markets. The separation between developed and emerging countries finds its

rationale in the fact that the different level of development may entail economic, financial,

political and regulatory conditions that are distinctive and typical for each of the two groups

but that are not directly measurable and hence could not be included in the regression as

explanatory variables. The pooled cross-pair time-series regression has also been estimated for

group of countries located in the same geographical zone. The rationale lies in the fact that

stock markets which have over-lapping trading hours, are more likely to systematically co-move

with each other on the same day than with markets in distant regions. Three regional areas

have been defined5: Europe including 21 pairs, Asia with 28 pairs and the Americas consisting of

10 pairs. Table 5.b presents pooled regression results for these three regions.

Two models have been estimated:

- Model 1 with all explanatory variables.

- Model 2 with all explanatory variables, except real interest rate differential, which proved

to be insignificant in the univariate regression results.

The results in Table 5.a and 5.b show that goodness-of-fit statistics, the adjusted R2 and the

F-statistic, indicate that the explanatory variables included in model 1 explain a significant

portion of stock markets co-movement on the same day. It should be noted that, for model

2, where the insignificant variable Real interest rate differential is removed, these measures

5 Europe: France, Germany, Italy, Sweden, UK, Russia, South Africa.

Asia: Australia, China, Japan, Hong Kong, Malaysia, India, Indonesia, Korea. Americas: Argentina, Brazil, Chile, Canada and USA.

26

of goodness of fit always increase; implying that model 2 better fits the underlying data.

Considering the all country-pairs regression, model 2 is able to explain almost 32 percent of

the variation in the contemporaneous Geweke feedback measure. When comparing this

measure between developed and emerging markets, it appears clear that the economic

integration among developed markets, as represented by the explanatory variables as well

as by intangible characteristics proper of this group, explains almost 50 percent of their

stock markets’ integration on the same day. The same is not valid for emerging markets, for

which only 30 percent of their same-day financial markets co-movement is explained by

economic integration. As suggested by Johnson & Soenen (2009), this greater extent of

developed countries’ stock market integration, apart from economic integration as proved

by regression results, may also be attributed to the presence of a more favorable economic

and political climate towards business in these countries compared to the emerging ones.

Table 5.a - Results of the pooled regressions on contemporaneous Geweke measures

Intercept -0.120 -15.884 *** 1.397 -38.485 *** -12.131 ** -9.012

Xi + 3.681 -48.065 -182.458 *** -433.392 *** 135.516 168.748

Xj + 30.563 106.214 728.302 *** 1546.485 *** 158.823 173.553

Mi ? 139.015 *** 157.836 *** 56.058 203.865 26.949 3.207

Mj ? 226.081 *** 384.402 *** 366.240 ** 209.265 218.517 206.651

Size - -19.560 -45.226 *** 127.165 *** 108.828 ** 582.777 *** 563.765 ***

log(MV) ? 3.736 *** 4.966 *** 10.913 ** 6.663 5.542 ** 4.435

Infllation - 0.076 0.024 -0.805 -1.915 -0.077 -0.029

GDP growth - 1.042 *** 1.730 *** -1.552 -0.548 0.444 0.233

Real interest - -0.120 -1.059 0.106

T + 6.600 *** 8.812 *** 9.080 *** 13.913 *** 5.919 *** 5.724 ***

# of obs 2450 2774 538 666 632 648

Adjusted R2

29.09% 31.69% 35.90% 49.74% 29.62% 29.88%

F-statistic 101.456 *** 143.950 *** 31.072 *** 74.110 *** 27.554 *** 31.639 ***

ALL COUNTRIES DEVELOPED EMERGINGExpected sign

Model 1 Model 2 Model 1 Model 2 Model 1 Model 2

** Significance at the 0.05 level. *** Significance at the 0.01 level.

When considering the three regional blocks, the adjusted R2 for model 2 is highest in Europe

with 70 percent of contemporaneous co-movement explained by economic integration. This

result is not surprising if one considers that European countries, excluding Russia and South

Africa, are part of an economic, political and monetary union. In the Asiatic region, economic

factors can explain almost 51 percent of the evolution of the Geweke feedback measure over

27

time whereas in the Americas only 32 percent of the variation in the contemporaneous

relationships over the 15-year sample is explained by economical integration.

Table 5.b - Results of the pooled regressions on contemporaneous Geweke measures

Intercept -28.251 -112.982 *** -12.414 ** -12.977 ** 40.778 ** 48.495 ***

Xi + 3211.827 *** 3704.471 *** 507.601 *** 518.355 *** 26.099 59.441

Xj + -193.390 -105.938 98.431 107.607 274.763 249.975

Mi ? -1968.281 *** -1728.129 *** -86.261 -105.775 -23.015 -33.315

Mj ? 384.582 289.103 -40.099 -33.097 -143.147 -189.185

Size - 882.419 ** 604.941 65.630 67.810 11.670 68.012

log(MV) ? 18.830 -2.980 8.961 *** 8.522 ** 8.115 11.843 **

Inflation - -0.078 0.289 -0.773 -0.483 0.577 0.116

GDP growth - 5.740 ** 10.909 *** 1.192 ** 1.186 ** -1.321 -1.731

Real interest - 0.400 -0.639 -0.100

T + 16.257 *** 25.013 *** 8.117 *** 8.106 *** 7.046 *** 6.749 ***

# of obs 214 297 413 413 142 150

Adjusted R2

52.53% 69.82% 50.81% 50.75% 30.40% 31.88%

F-statistic 24.575 *** 77.103 *** 43.550 *** 48.178 *** 7.157 *** 8.749 ***

Model 1 Model 2

EUROPE ASIA AMERICAS

Model 1 Model 2 Model 1 Model 2

Expected sign

** Significance at the 0.05 level. *** Significance at the 0.01 level.

The variables included in the pooled regressions are always jointly significant at the 1 percent

level and, on average, four of the ten explanatory variables enter into model 2 at 5 percent

level of significance. More specifically, one or several measures of bilateral trade relationships

are able to significantly influence stock market integration over time in the developed markets’

group, in the European and Asiatic regions as well as in the all countries’ sample. This finding

does not conflict with the empirical results provided by Braker et al. (1999) nor with those

presented by Johnson and Soenen (2002; 2003). In these studies, indicators of bilateral trade

are found to be significantly associated with the evolution of stock market integration over

time. The interesting result that inflation rate differential and real interest rate differential are

never statistically significant in influencing the variation of the contemporaneous Geweke

measures is also in accordance with these studies, excluding Johnson and Soenen (2002) that

found greater differential inflation and differential real interest to be significant in reducing the

co-movement among Asian markets. When the measures of financial development are

considered, results are less consistent with those from other studies. Size differential is found

28

to be statistically significant in the all countries’ sample as well as in the developed and

emerging markets’ groups, whereas it is not in the regional blocks. Bracker et al. (1999) show

that, in a group of developed markets, size differential is statistically significant in explaining the

degree of contemporaneous co-movement. However, in the study conducted by Bracker et al.

(1999), a greater size differential negatively affects stock markets’ interdependence, as

expected, whereas in our study this effect is positive. The relative size indicator (natural

logarithm of the variable MV, as described in table 1) is positively associated with greater

contemporaneous co-movement in the all countries’ sample, in the Pacific region and in the

Americas. Conversely, this indicator is negatively related to stock markets’ integration in the

American block according to Johnson and Soenen (2003) and it is not significant among Asian

countries according to Johnson and Soenen (2002). Finally, all regions and all groups of

countries always exhibit a significant trend towards increasing same-day co-movement over

time throughout the 15-year sample, as also empirically demonstrated by other studies.

5. Conclusions

This study examines the degree of cross-country returns co-movement between the stock

markets of five developed [US, UK, Japan, Germany and France] and five emerging [Brazil,

Russia, India, China and South Africa] countries, vis-à-vis a total country sample composed by

20 countries. The Geweke (1982) Measure of Feedback methodology along with a set of pooled

cross-country time-series regressions is used to identify and explain the changes in stock

market integration. The general findings for the Geweke contemporaneous feedback measures

provide supportive evidence of increased stock market integration. A reasonably clear time

trend is identified, where the extent of contemporaneous co-movement across markets has

intensified over time, especially for emerging countries, which consequently suggests that

greater market efficiency is being fostered at the international level. On the other hand, the

results of the Geweke unidirectional feedback measures indicate a tendency that some markets

are more likely to lead other markets than vice versa. However, there is a less distinctive time

trend in the movements of the annual two-ways unidirectional feedback measures, suggesting

that leader-follower relationships are likely to change over time periods. These alterations

29

might be due to possible changes in a country’s economy and market conditions, but also the

stability of global markets. Nevertheless, the highly sophisticated market of the US and the

emerging markets of Brazil and Russia appear to affect other rather than be affected. Similar

findings have been reported for the US by Eun and Shim (1991), Bracker et al. (1999) and

Johnson and Soenen (2003). However, the distinction with previous studies is that not only is

the contemporaneous measures larger on average, but higher significance levels are reported

for the unidirectional measures of feedback, suggesting that cross-market adjustment persist

over time more often than occasionally for this study’s time period 1995-2010. Our pooled

cross-pair time-series regression analysis has shown that countries’ economic integration, as

measured by the explanatory variables included in model 2, can explain almost 32 percent of

the variation in the contemporaneous Geweke feedback measure on a global scale over the 15-

year period, 1995-2009. This explanatory power becomes stronger for the group of developed

markets (49.74 percent) and for countries that are part of the European economic and political

union (69.82 percent). The results also point out several variables as significantly associated

with the evolution of stock markets integration over time. These statistically significant

variables include, on a global level, import dependence, stock markets’ size differential and

their relative size, difference in annual GDP growth rate as well as the time trend.

References

Ampomah, S.A., (2008), An empirical examination of the inter-linkages between African stock

markets. University of Surrey, Guildford, United Kingdom, Electronic copy available at:

http://ssrn.com/abstract=1311325.

Arshanapalli, B., Doukas, J., Lang, L.H.P., (1995), Pre and post-October 1987 stock market

linkages between U.S. and Asian markets. Pacific-Basin Financial Journal, 3, 57-73.

Bracker, K., Docking, D.S., Koch, P.D., (1999), Economic determinants of evolution in

international stock market integration. Journal of Empirical Finance, 6, 1-27.

30

Bracker, K., Koch, P.D., (1999) Economic determinants of the correlation structure across

international equity markets. Journal of Economics and Business, 51, 443-471.

Choudry, T., (1997) Stochastic Trends in Stock Prices: Evidence from Latin American Markets.

Journal of Macroeconomics, 19(2), 285–304.

Eun, C.S., Shim,S., (1989), International Transmission of Stock Market Movements. The Journal

of Financial and Quantitative Analysis, 24(2), 241-256.

Forbes, K.J., Rigobon, R., (2002), No contagion, only interdependence: Measuring stock market

comovements, The Journal of Finance, 57(5), 2223-2261.

Geweke, J., (1982), Measurement of linear dependence and feedback between multiple time

series, Journal of the American Statistical Association, 77(378), 304.

Geweke, J., (1984), Measures of conditional linear dependence and feedback between time

series, Journal of the American Statistical Association, 79(388), 907.

Johnson, R., Soenen, L., (2009), Equity market risk premium and global integration, Journal of

Centrum Cathedra, 2, 12-22

Johnson, R., Soenen, L., (2003),. Economic integration and stock market comovement in the

Americas. Journal of multinational financial management, 13, 85-100.

Johnson, R., Soenen, L., (2002), Asian economic integration and stock market comovement. The

Journal of Financial Research, 25(1), 141-157.

Karolyi, G.A., Stulz, R.M., (1996), Why Do Markets Move Together? An Investigation of U.S.-

Japan Stock Return Comovements. The Journal of Finance, 51(3), 951-986.

Kunst, Robert M.,(2009), “Econometric Methods for Panel data-Part II”, University of Vienna, http:\\homepage.univie.ac.at\robert.kunst\panels2e.pdf.

Lin, W.L., Engel, R.F., Ito, T., (1994), Do Bulls and Bears Move Across Borders? International

Transmission of Stock Returns and Volatility. The Review of Financial Studies, 7(3), 507-538.

31

Morgado, P., Tavares, J., (2007), Economic Integration and the Co-Movement of Stock Returns.

CEPR Discussion Paper No. DP6519. Available at SSRN: http://ssrn.com/abstract=1140046

Mukherjee, T.K., Naka, A., (1995), Dynamic relations between macroeconomic variables and the

Japanese stock market: An application of a vector error correction model. Journal of Financial

Research, 18, 223-237.

Lin, C.H., Cheng, W.H., (2008), Economic determinants of co-movement across international

stock markets: the example of Taiwan and its key trading partners, Applied Economics, 40,

1187-1205.

Liu, Y.A., Pan, M.S., Shieh, J.C.P. (1998), International Transmission of Stock Price Movements:

Evidence from the U.S. and Five Asian-Pacific Markets. Journal of Economics and Finance, 22(1),

59-69.

Longin, F., Solnik, B. (1995), Is the correlation in international equity returns constant: 1960-

1990?. Journal of International Money and Finance, 14(1), 3-26.

Pretorious, E. (2002), Economic determinants of emerging Stock Market Interdependence.

Emerging Markets Review, 3, 84-105.

Serra, A.P., (2000), Country and industry factors in returns: evidence from emerging markets’

stocks. Emerging Markets Review, 1, 127-151.

Solnik, B.H., (1995), Why Not Diversify Internationally Rather than Domestically?. Financial

Analysts Journal, 51(1), 89-94.

Wälti, S,. (2005) The macroeconomic determinants of stock market synchronization.

Department of Economics, Trinity College Dublin, Dublin 2, Ireland.

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Appendix

A. Geweke Measure of Feedback

A1. United States

33

34

35

A2. United Kingdom

36

37

38

A3. Japan

39

40

41

A4. Germany

42

43

44

A5. France

45

46

47

A6. Brazil

48

49

50

A7. Russia

51

52

53

A8. India

54

55

56

A9. China

57

58

59

A10. South Africa

60

61